Explore the vast range of titles available.

Although biomimetic designs are expected to play a key role in exploring future structural materials, facile fabrication of bulk biomimetic materials under ambient conditions remains a major challenge. Here, we describe a mesoscale \"assembly-and-mineralization\" approach inspired by the natural process in mollusks to fabricate bulk synthetic nacre that highly resembles both the chemical composition and the hierarchical structure of natural nacre. The millimeter-thick synthetic nacre consists of alternating organic layers and aragonite platelet layers (91 weight percent) and exhibits good ultimate strength and fracture toughness. This predesigned matrix-directed mineralization method represents a rational strategy for the preparation of robust composite materials with hierarchically ordered structures, where various constituents are adaptable, including brittle and heat-labile materials.

Advances in mechanistic knowledge of human neurological disorders have been hindered by the lack of adequate human in vitro models. Three-dimensional (3D) cellular models displaying higher biological relevance are gaining momentum; however, their lack of robustness and scarcity of analytical tools adapted to three dimensions hampers their widespread implementation. Herein we show that human midbrain-derived neural progenitor cells, cultured as 3D neurospheres in stirred culture systems, reproducibly differentiate into complex tissue-like structures containing functional dopaminergic neurons, as well as astrocytes and oligodendrocytes. Moreover, an extensive toolbox of analytical methodologies has been adapted to 3D neural cell models, allowing molecular and phenotypic profiling and interrogation. The generated neurons underwent synaptogenesis and elicit spontaneous Ca 2+ transients. Synaptic vesicle trafficking and release of dopamine in response to depolarizing stimuli was also observed. Under whole-cell current-and-voltage clamp, recordings showed polarized neurons ( V m =−70 mV) and voltage-dependent potassium currents, which included A-type-like currents. Glutamate-induced currents sensitive to α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid and N-methyl-D-aspartate antagonists revealed the existence of functional glutamate receptors. Molecular and phenotypic profiling showed recapitulation of midbrain patterning events, and remodeling toward increased similarity to human brain features, such as extracellular matrix composition and metabolic signature. We have developed a robust and reproducible human 3D neural cell model, which may be extended to patient-derived induced pluripotent stem cells, broadening the applicability of this model.

Helical structures are ubiquitous in nature and impart unique mechanical properties and multifunctionality 1 . So far, synthetic architectures that mimic these natural systems have been fabricated by winding, twisting and braiding of individual filaments 1 – 7 , microfluidics 8 , 9 , self-shaping 1 , 10 – 13 and printing methods 14 – 17 . However, those fabrication methods are unable to simultaneously create and pattern multimaterial, helically architected filaments with subvoxel control in arbitrary two-dimensional (2D) and three-dimensional (3D) motifs from a broad range of materials. Towards this goal, both multimaterial 18 – 23 and rotational 24 3D printing of architected filaments have recently been reported; however, the integration of these two capabilities has yet to be realized. Here we report a rotational multimaterial 3D printing (RM-3DP) platform that enables subvoxel control over the local orientation of azimuthally heterogeneous architected filaments. By continuously rotating a multimaterial nozzle with a controlled ratio of angular-to-translational velocity, we have created helical filaments with programmable helix angle, layer thickness and interfacial area between several materials within a given cylindrical voxel. Using this integrated method, we have fabricated functional artificial muscles composed of helical dielectric elastomer actuators with high fidelity and individually addressable conductive helical channels embedded within a dielectric elastomer matrix. We have also fabricated hierarchical lattices comprising architected helical struts containing stiff springs within a compliant matrix. Our additive-manufacturing platform opens new avenues to generating multifunctional architected matter in bioinspired motifs. A 3D printing platform comprising a rotational multimaterial printhead is demonstrated, enabling the fabrication of helically architected filaments and lattices with programmable subvoxel control.

Current hardware approaches to biomimetic or neuromorphic artificial intelligence rely on elaborate transistor circuits to simulate biological functions. However, these can instead be more faithfully emulated by higher-order circuit elements that naturally express neuromorphic nonlinear dynamics 1 – 4 . Generating neuromorphic action potentials in a circuit element theoretically requires a minimum of third-order complexity (for example, three dynamical electrophysical processes) 5 , but there have been few examples of second-order neuromorphic elements, and no previous demonstration of any isolated third-order element 6 – 8 . Using both experiments and modelling, here we show how multiple electrophysical processes—including Mott transition dynamics—form a nanoscale third-order circuit element. We demonstrate simple transistorless networks of third-order elements that perform Boolean operations and find analogue solutions to a computationally hard graph-partitioning problem. This work paves a way towards very compact and densely functional neuromorphic computing primitives, and energy-efficient validation of neuroscientific models. Electrophysical processes are used to create third-order nanoscale circuit elements, and these are used to realize a transistorless network that can perform Boolean operations and find solutions to a computationally hard graph-partitioning problem.

Drawing inspiration from cohesive integration of skeletal muscles and sensory skins in vertebrate animals, we present a design strategy of soft robots, primarily consisting of an electronic skin (e-skin) and an artificial muscle. These robots integrate multifunctional sensing and on-demand actuation into a biocompatible platform using an in-situ solution-based method. They feature biomimetic designs that enable adaptive motions and stress-free contact with tissues, supported by a battery-free wireless module for untethered operation. Demonstrations range from a robotic cuff for detecting blood pressure, to a robotic gripper for tracking bladder volume, an ingestible robot for pH sensing and on-site drug delivery, and a robotic patch for quantifying cardiac function and delivering electrotherapy, highlighting the application versatilities and potentials of the bio-inspired soft robots. Our designs establish a universal strategy with a broad range of sensing and responsive materials, to form integrated soft robots for medical technology and beyond. Integrating sensing and actuation capabilities in soft robots is crucial for advancements in medical diagnostics and targeted therapies. Zhang et al. developed bio-inspired sensory robots with multifunctionality for minimally invasive medical procedures.

Effective, long-range, and self-propelled water elevation and transport are important in industrial, medical, and agricultural applications. Although research has grown rapidly, existing methods for water film elevation are still limited. Scaling up for practical applications in an energy-efficient way remains a challenge. Inspired by the continuous water cross-boundary transport on the peristome surface of Nepenthes alata, here we demonstrate the use of peristome-mimetic structures for controlled water elevation by bending biomimetic plates into tubes. The fabricated structures have unique advantages beyond those of natural pitcher plants: bulk water diode transport behavior is achieved with a high-speed passing state (several centimeters per second on a milliliter scale) and a gating state as a result of the synergistic effect between peristome-mimetic structures and tube curvature without external energy input. Significantly, on further bending the peristome-mimetic tube into a “candy cane”-shaped pipe, a self-siphon with liquid diode behavior is achieved. Such a transport mechanism should inspire the design of next generation water transport devices.

An intelligent trial-and-error learning algorithm is presented that allows robots to adapt in minutes to compensate for a wide variety of types of damage. Robots built to adapt Autonomous mobile robots would be extremely useful in remote or hostile environments such as space, deep oceans or disaster areas. An outstanding challenge is to make such robots able to recover after damage. Jean-Baptiste Mouret and colleagues have developed a machine learning algorithm that enables damaged robots to quickly regain their ability to perform tasks. When they sustain damage — such as broken or even missing legs — the robots adopt an intelligent trial-and-error approach, trying out possible behaviours that they calculate to be potentially high-performing. After a handful of such experiments they discover, in less than two minutes, a compensatory behaviour that works in spite of the damage. Robots have transformed many industries, most notably manufacturing 1 , and have the power to deliver tremendous benefits to society, such as in search and rescue 2 , disaster response 3 , health care 4 and transportation 5 . They are also invaluable tools for scientific exploration in environments inaccessible to humans, from distant planets 6 to deep oceans 7 . A major obstacle to their widespread adoption in more complex environments outside factories is their fragility 6 , 8 . Whereas animals can quickly adapt to injuries, current robots cannot ‘think outside the box’ to find a compensatory behaviour when they are damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes 9 , and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots 6 , 8 . A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage 10 , 11 , but current techniques are slow even with small, constrained search spaces 12 . Here we introduce an intelligent trial-and-error algorithm that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans. Before the robot is deployed, it uses a novel technique to create a detailed map of the space of high-performing behaviours. This map represents the robot’s prior knowledge about what behaviours it can perform and their value. When the robot is damaged, it uses this prior knowledge to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a behaviour that compensates for the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new algorithm will enable more robust, effective, autonomous robots, and may shed light on the principles that animals use to adapt to injury.

The functional maturation and preservation of hepatic cells derived from human induced pluripotent stem cells (hiPSCs) are essential to personalized in vitro drug screening and disease study. Major liver functions are tightly linked to the 3D assembly of hepatocytes, with the supporting cell types from both endodermal and mesodermal origins in a hexagonal lobule unit. Although there are many reports on functional 2D cell differentiation, few studies have demonstrated the in vitro maturation of hiPSC-derived hepatic progenitor cells (hiPSC-HPCs) in a 3D environment that depicts the physiologically relevant cell combination and microarchitecture. The application of rapid, digital 3D bioprinting to tissue engineering has allowed 3D patterning of multiple cell types in a predefined biomimetic manner. Here we present a 3D hydrogel-based triculture model that embeds hiPSC-HPCs with human umbilical vein endothelial cells and adipose-derived stem cells in a microscale hexagonal architecture. In comparison with 2D monolayer culture and a 3D HPC-only model, our 3D triculture model shows both phenotypic and functional enhancements in the hiPSC-HPCs over weeks of in vitro culture. Specifically, we find improved morphological organization, higher liver-specific gene expression levels, increased metabolic product secretion, and enhanced cytochrome P450 induction. The application of bioprinting technology in tissue engineering enables the development of a 3D biomimetic liver model that recapitulates the native liver module architecture and could be used for various applications such as early drug screening and disease modeling.

After half a billion years of evolution, arthropods have developed sophisticated compound eyes with extraordinary visual capabilities that have inspired the development of artificial compound eyes. However, the limited 2D nature of most traditional fabrication techniques makes it challenging to directly replicate these natural systems. Here, we present a biomimetic apposition compound eye fabricated using a microfluidic-assisted 3D-printing technique. Each microlens is connected to the bottom planar surface of the eye via intracorporal, zero-crosstalk refractive-index-matched waveguides to mimic the rhabdoms of a natural eye. Full-colour wide-angle panoramic views and position tracking of a point source are realized by placing the fabricated eye directly on top of a commercial imaging sensor. As a biomimetic analogue to naturally occurring compound eyes, the eye’s full-colour 3D to 2D mapping capability has the potential to enable a wide variety of applications from improving endoscopic imaging to enhancing machine vision for facilitating human–robot interactions. Insect-like biomimetic compound eyes have many technological applications. Here, the authors present a facile fabrication scheme involving microfluidics assisted 3D printing that permits to completely separate design, optimization and construction of optical and sensor components.

'Organs-on-chips' are microengineered biomimetic systems containing microfluidic channels lined by living human cells, which replicate key functional units of living organs to reconstitute integrated human organ-level pathophysiology in vitro . These microdevices can be used to test efficacy and toxicity of drugs and chemicals, and to create in vitro models of human disease. Thus, they potentially represent low-cost alternatives to conventional animal models for pharmaceutical, chemical and environmental applications. Here we describe a protocol for the fabrication, microengineering and operation of these microfluidic organ-on-chip systems. First, microengineering is used to fabricate a multilayered microfluidic device that contains two parallel elastomeric microchannels separated by a thin porous flexible membrane, along with two full-height, hollow vacuum chambers on either side; this requires ∼3.5 d to complete. To create a 'breathing' lung-on-a-chip that mimics the mechanically active alveolar-capillary interface of the living human lung, human alveolar epithelial cells and microvascular endothelial cells are cultured in the microdevice with physiological flow and cyclic suction applied to the side chambers to reproduce rhythmic breathing movements. We describe how this protocol can be easily adapted to develop other human organ chips, such as a gut-on-a-chip lined by human intestinal epithelial cells that experiences peristalsis-like motions and trickling fluid flow. Also, we discuss experimental techniques that can be used to analyze the cells in these organ-on-chip devices.